Python box_list_ops.boolean_mask方法代码示例

# 需要导入模块: from object_detection.core import box_list_ops [as 别名]
# 或者: from object_detection.core.box_list_ops import boolean_mask [as 别名]
def test_boolean_mask_with_field(self):
    corners = tf.constant(
        [4 * [0.0], 4 * [1.0], 4 * [2.0], 4 * [3.0], 4 * [4.0]])
    indicator = tf.constant([True, False, True, False, True], tf.bool)
    weights = tf.constant([[.1], [.3], [.5], [.7], [.9]], tf.float32)
    expected_subset = [4 * [0.0], 4 * [2.0], 4 * [4.0]]
    expected_weights = [[.1], [.5], [.9]]

    boxes = box_list.BoxList(corners)
    boxes.add_field('weights', weights)
    subset = box_list_ops.boolean_mask(boxes, indicator, ['weights'])
    with self.test_session() as sess:
      subset_output, weights_output = sess.run(
          [subset.get(), subset.get_field('weights')])
      self.assertAllClose(subset_output, expected_subset)
      self.assertAllClose(weights_output, expected_weights) 

# 需要导入模块: from object_detection.core import box_list_ops [as 别名]
# 或者: from object_detection.core.box_list_ops import boolean_mask [as 别名]
def _get_refined_encodings_for_postitive_class(
      self, refined_box_encodings, flat_cls_targets_with_background,
      batch_size):
    # We only predict refined location encodings for the non background
    # classes, but we now pad it to make it compatible with the class
    # predictions
    refined_box_encodings_with_background = tf.pad(refined_box_encodings,
                                                   [[0, 0], [1, 0], [0, 0]])
    refined_box_encodings_masked_by_class_targets = (
        box_list_ops.boolean_mask(
            box_list.BoxList(
                tf.reshape(refined_box_encodings_with_background,
                           [-1, self._box_coder.code_size])),
            tf.reshape(tf.greater(flat_cls_targets_with_background, 0), [-1]),
            use_static_shapes=self._use_static_shapes,
            indicator_sum=batch_size * self.max_num_proposals
            if self._use_static_shapes else None).get())
    return tf.reshape(
        refined_box_encodings_masked_by_class_targets, [
            batch_size, self.max_num_proposals,
            self._box_coder.code_size
        ]) 

# 需要导入模块: from object_detection.core import box_list_ops [as 别名]
# 或者: from object_detection.core.box_list_ops import boolean_mask [as 别名]
def test_static_boolean_mask_with_field(self):

    def graph_fn(corners, weights, indicator):
      boxes = box_list.BoxList(corners)
      boxes.add_field('weights', weights)
      subset = box_list_ops.boolean_mask(
          boxes,
          indicator, ['weights'],
          use_static_shapes=True,
          indicator_sum=3)
      return (subset.get_field('boxes'), subset.get_field('weights'))

    corners = np.array(
        [4 * [0.0], 4 * [1.0], 4 * [2.0], 4 * [3.0], 4 * [4.0]],
        dtype=np.float32)
    indicator = np.array([True, False, True, False, True], dtype=np.bool)
    weights = np.array([[.1], [.3], [.5], [.7], [.9]], dtype=np.float32)
    result_boxes, result_weights = self.execute(graph_fn,
                                                [corners, weights, indicator])
    expected_boxes = [4 * [0.0], 4 * [2.0], 4 * [4.0]]
    expected_weights = [[.1], [.5], [.9]]

    self.assertAllClose(result_boxes, expected_boxes)
    self.assertAllClose(result_weights, expected_weights) 

# 需要导入模块: from object_detection.core import box_list_ops [as 别名]
# 或者: from object_detection.core.box_list_ops import boolean_mask [as 别名]
def test_dynamic_boolean_mask_with_field(self):
    corners = tf.placeholder(tf.float32, [None, 4])
    indicator = tf.placeholder(tf.bool, [None])
    weights = tf.placeholder(tf.float32, [None, 1])
    expected_subset = [4 * [0.0], 4 * [2.0], 4 * [4.0]]
    expected_weights = [[.1], [.5], [.9]]

    boxes = box_list.BoxList(corners)
    boxes.add_field('weights', weights)
    subset = box_list_ops.boolean_mask(boxes, indicator, ['weights'])
    with self.test_session() as sess:
      subset_output, weights_output = sess.run(
          [subset.get(), subset.get_field('weights')],
          feed_dict={
              corners:
                  np.array(
                      [4 * [0.0], 4 * [1.0], 4 * [2.0], 4 * [3.0], 4 * [4.0]]),
              indicator:
                  np.array([True, False, True, False, True]).astype(np.bool),
              weights:
                  np.array([[.1], [.3], [.5], [.7], [.9]])
          })
      self.assertAllClose(subset_output, expected_subset)
      self.assertAllClose(weights_output, expected_weights) 

# 需要导入模块: from object_detection.core import box_list_ops [as 别名]
# 或者: from object_detection.core.box_list_ops import boolean_mask [as 别名]
def test_boolean_mask_with_field(self):
        corners = tf.constant(
            [4 * [0.0], 4 * [1.0], 4 * [2.0], 4 * [3.0], 4 * [4.0]])
        indicator = tf.constant([True, False, True, False, True], tf.bool)
        weights = tf.constant([[.1], [.3], [.5], [.7], [.9]], tf.float32)
        expected_subset = [4 * [0.0], 4 * [2.0], 4 * [4.0]]
        expected_weights = [[.1], [.5], [.9]]

        boxes = box_list.BoxList(corners)
        boxes.add_field('weights', weights)
        subset = box_list_ops.boolean_mask(boxes, indicator, ['weights'])
        with self.test_session() as sess:
            subset_output, weights_output = sess.run(
                [subset.get(), subset.get_field('weights')])
            self.assertAllClose(subset_output, expected_subset)
            self.assertAllClose(weights_output, expected_weights)